Studying the stability of polymer nanoparticles by size exclusion chromatography of radioactive polymers
Bibliographic record
Abstract
The properties of nanomedicines will influence how they can deliver drugs to patients reproducibly and effectively. For conventional pharmaceutical products, Chemistry, Manufacturing and Control (CMC) documents require monitoring stability and storage conditions. For nanomedicines, studying these important considerations is hindered by a lack of appropriate methods. In this paper, we show how combining radiolabelling with size exclusion chromatography, using a method called SERP (for Size Exclusion of Radioactive Polymers), can inform on the in vitro degradation of polymer nanoparticles. Using nanoparticles composed of biodegradable poly(lactic acid) (PLA) and poly(lactic-co-glycolic acid) (PLGA), we show that SERP is more sensitive than dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) to detect degradation. We also demonstrate that the properties of the polymer composition and the nature of the aqueous buffer affect nanoparticle degradation. Importantly, we show that minute changes in stability that cannot be detected by DLS and NTA impact the pharmacokinetic of nanoparticles injected in vivo. We believe that SERP might prove a valuable method to document and understand the pharmaceutical quality of polymer nanoparticles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".